Microsoft and Antha: Closed Loop Design, Build, Test and Learn Engineering Principles for Synthetic Genetic Networks
公司规模
1,000+
地区
- Europe
国家
- United Kingdom
产品
- Antha
- Station B Platform
技术栈
- Machine Learning
- Bayesian Inference
- Markov Chain Monte Carlo (MCMC)
- Ordinary Differential Equations (ODE)
实施规模
- Departmental Deployment
影响指标
- Digital Expertise
- Innovation Output
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 功能应用 - 远程监控系统
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- 医疗保健和医院
- 生命科学
适用功能
- 产品研发
- 质量保证
用例
- 数字孪生
- 机器状态监测
- 预测性维护
- 远程控制
服务
- 软件设计与工程服务
- 系统集成
关于客户
Microsoft Research Cambridge, UK, was the first Microsoft Research lab established outside of the United States. Since its inception in 1997 with just three researchers, it has grown to over 130 researchers and engineers. The lab has established itself as a credible academic partner and a source of new ideas that have been integrated into Microsoft's core business. The lab's diverse team includes sociologists, mathematicians, computer scientists, designers, and biologists, contributing to some of Microsoft's most successful products and services. The Biological Computation group at Microsoft Research began working with Synthace in 2014, using Antha as part of its research into synthetic biology methods and applications.
挑战
The Biological Computation group at Microsoft Research in Cambridge faced the challenge of developing an integrated platform for programming biological systems more effectively. The goal was to improve all phases of the Design, Build, Test, Learn (DBTL) workflow typically used in Synthetic Biology. The main bottleneck was the physical build and test stages in the laboratory, which required a rapid and flexible method for programming the laboratory hardware. The challenge was to accelerate the DBTL cycle without compromising on experimental flexibility, and to generate sufficient breadth of data through complex experimental designs executed in a reproducible manner.
解决方案
Microsoft Research interfaced its Station B platform with Antha to leverage its experimental simulation and physical execution functionality. Antha enabled the digital to physical transition of the DBTL cycle, automating the process and ensuring data was structured appropriately for machine learning workflows. The Station B platform facilitated the combinatorial design of genetic devices, which were then physically executed on liquid handling automation platforms controlled by Antha. This integration allowed Microsoft scientists to accelerate the DBTL cycle, focusing on refining biological designs and applying computational analysis rather than manual lab operations. Antha's optimised Construct Assembly workflows enabled rapid and flexible programming of liquid handling platforms, driving the physical execution of genetic construct assemblies and subsequent automated transformation and plating out of transformants.
运营影响
数量效益
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